scholarly journals The recyclability benefit rate of closed-loop and open-loop systems: A case study on plastic recycling in Flanders

2015 ◽  
Vol 101 ◽  
pp. 53-60 ◽  
Author(s):  
Sofie Huysman ◽  
Sam Debaveye ◽  
Thomas Schaubroeck ◽  
Steven De Meester ◽  
Fulvio Ardente ◽  
...  
Keyword(s):  
Closed Loop ◽  
Open Loop ◽  
Plastic Recycling

Identification of Multi-Input Multi-Output Systems Using Combined Direct and Indirect Methods

2014 ◽  
Vol 625 ◽  
pp. 414-417
Author(s):  
Abdelraheem Faisal ◽  
Marappagounder Ramasamy ◽  
Mahadzir Shuhaimi ◽  
Mohamed Rahim
Keyword(s):  
Closed Loop ◽  
Mimo Systems ◽  
Open Loop ◽  
Identification Procedure ◽  
Indirect Methods ◽  
Representative Case ◽  
Closed Loop Identification ◽  
Decentralized Model ◽  
Direct And Indirect Methods

Successful deployment of cooperative decentralized model predicative control needs reasonably accurate subsystem interactions models. Processes in which open-loop tests are not permitted, closed-loop identification of subsystems interactions is crucial. An approach that combines the direct and indirect methods of closed-loop identification is proposed in this paper. It is shown that full dynamics of MIMO systems can be determined following a two-steps identification procedure. A representative case study is used to demonstrate the efficacy of the proposed approach.

Closed-Loop Predictions in Reservoir Management Under Uncertainty

SPE Journal ◽  
2017 ◽  
Vol 22 (05) ◽  
pp. 1585-1595 ◽  
Author(s):  
Kristian G. Hanssen ◽  
Andrés Codas ◽  
Bjarne Foss
Keyword(s):  
Closed Loop ◽  
Reservoir Management ◽  
Open Loop ◽  
Algebraic Equations ◽  
Control Policies ◽  
Reservoir Optimization ◽  
Significant Potential ◽  
Prediction Horizon ◽  
Gradient Calculation

Summary Uncertainty is a major challenge in reservoir management. To take the uncertainty into consideration, optimization can be carried out over a set of scenarios. Most approaches on reservoir management under uncertainty optimize a sequence of control inputs applied to all scenarios over the prediction horizon; hence, they are open-loop predictions. In this paper, we optimize over control policies, as opposed to a sequence of control inputs, to obtain closed-loop predictions. The policies are specified as a set of implicit algebraic equations, allowing for efficient gradient calculation by an adjoint simulation. The method is compared with the more traditional open-loop approach in a case study, indicating a significant potential for reservoir optimization by use of closed-loop predictions.

On closed-loop equilibrium strategies for mean-field stochastic linear quadratic problems

2020 ◽  
Vol 26 ◽  
pp. 41
Author(s):  
Tianxiao Wang
Keyword(s):  
Optimal Control ◽  
Closed Loop ◽  
Optimal Control Problems ◽  
Mean Field ◽  
Open Loop ◽  
Time Inconsistency ◽  
Control Problems ◽  
Linear Quadratic ◽  
Linear Quadratic Optimal Control ◽  
Equilibrium Strategies

This article is concerned with linear quadratic optimal control problems of mean-field stochastic differential equations (MF-SDE) with deterministic coefficients. To treat the time inconsistency of the optimal control problems, linear closed-loop equilibrium strategies are introduced and characterized by variational approach. Our developed methodology drops the delicate convergence procedures in Yong [Trans. Amer. Math. Soc. 369 (2017) 5467–5523]. When the MF-SDE reduces to SDE, our Riccati system coincides with the analogue in Yong [Trans. Amer. Math. Soc. 369 (2017) 5467–5523]. However, these two systems are in general different from each other due to the conditional mean-field terms in the MF-SDE. Eventually, the comparisons with pre-committed optimal strategies, open-loop equilibrium strategies are given in details.

Comparison of Dc-Dc SEPIC, CUK and Flyback Converters Based LED Drivers

2020 ◽  
pp. 99-107
Author(s):  
Erdal Sehirli
Keyword(s):  
Integrated Circuit ◽  
Closed Loop ◽  
Input Voltage ◽  
Open Loop ◽  
Current Sensor ◽  
Switching Frequency ◽  
Led Driver ◽  
Advantages And Disadvantages ◽  
Led Drivers ◽  
Conduction Mode

This paper presents the comparison of LED driver topologies that include SEPIC, CUK and FLYBACK DC-DC converters. Both topologies are designed for 8W power and operated in discontinuous conduction mode (DCM) with 88 kHz switching frequency. Furthermore, inductors of SEPIC and CUK converters are wounded as coupled. Applications are realized by using SG3524 integrated circuit for open loop and PIC16F877 microcontroller for closed loop. Besides, ACS712 current sensor used to limit maximum LED current for closed loop applications. Finally, SEPIC, CUK and FLYBACK DC-DC LED drivers are compared with respect to LED current, LED voltage, input voltage and current. Also, advantages and disadvantages of all topologies are concluded.

Developing a Cutting Edge, Closed-Loop Organics-to-Energy Project – Roseville Case Study

2018 ◽  
Vol 2018 (7) ◽  
pp. 5417-5435
Author(s):  
Alison Nojima ◽  
Adam Ross ◽  
Ken Glotzbach ◽  
Todd Jordan ◽  
George Hanson
Keyword(s):  
Closed Loop ◽  
Cutting Edge

scholarly journals Parallel Point Clouds: Hybrid Point Cloud Generation and 3D Model Enhancement via Virtual–Real Integration

2021 ◽  
Vol 13 (15) ◽  
pp. 2868
Author(s):  
Yonglin Tian ◽  
Xiao Wang ◽  
Yu Shen ◽  
Zhongzheng Guo ◽  
Zilei Wang ◽  
...  
Keyword(s):  
Point Cloud ◽  
Closed Loop ◽  
Real Data ◽  
Point Clouds ◽  
Autonomous Driving ◽  
Training Model ◽  
Labor Cost ◽  
Open Loop ◽  
Training Dataset ◽  
Data Annotation

Three-dimensional information perception from point clouds is of vital importance for improving the ability of machines to understand the world, especially for autonomous driving and unmanned aerial vehicles. Data annotation for point clouds is one of the most challenging and costly tasks. In this paper, we propose a closed-loop and virtual–real interactive point cloud generation and model-upgrading framework called Parallel Point Clouds (PPCs). To our best knowledge, this is the first time that the training model has been changed from an open-loop to a closed-loop mechanism. The feedback from the evaluation results is used to update the training dataset, benefiting from the flexibility of artificial scenes. Under the framework, a point-based LiDAR simulation model is proposed, which greatly simplifies the scanning operation. Besides, a group-based placing method is put forward to integrate hybrid point clouds, via locating candidate positions for virtual objects in real scenes. Taking advantage of the CAD models and mobile LiDAR devices, two hybrid point cloud datasets, i.e., ShapeKITTI and MobilePointClouds, are built for 3D detection tasks. With almost zero labor cost on data annotation for newly added objects, the models (PointPillars) trained with ShapeKITTI and MobilePointClouds achieved 78.6% and 60.0% of the average precision of the model trained with real data on 3D detection, respectively.

scholarly journals Converting MST to TSP Path by Branch Elimination

2020 ◽  
Vol 11 (1) ◽  
pp. 177
Author(s):  
Pasi Fränti ◽  
Teemu Nenonen ◽  
Mingchuan Yuan
Keyword(s):  
Spanning Tree ◽  
Closed Loop ◽  
Minimum Spanning Tree ◽  
Travelling Salesman Problem ◽  
Open Loop ◽  
Travelling Salesman ◽  
Elimination Algorithm ◽  
Number Of Iterations ◽  
Number Of Branches

Travelling salesman problem (TSP) has been widely studied for the classical closed loop variant but less attention has been paid to the open loop variant. Open loop solution has property of being also a spanning tree, although not necessarily the minimum spanning tree (MST). In this paper, we present a simple branch elimination algorithm that removes the branches from MST by cutting one link and then reconnecting the resulting subtrees via selected leaf nodes. The number of iterations equals to the number of branches (b) in the MST. Typically, b << n where n is the number of nodes. With O-Mopsi and Dots datasets, the algorithm reaches gap of 1.69% and 0.61 %, respectively. The algorithm is suitable especially for educational purposes by showing the connection between MST and TSP, but it can also serve as a quick approximation for more complex metaheuristics whose efficiency relies on quality of the initial solution.

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